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Abstract We present a lightweight, easy‐to‐train, low‐resolution, fully data‐driven climate emulator, LUCIE, that can be trained on as low as 2 years of 6‐hourly ERA5 data. Unlike most state‐of‐the‐art AI weather models, LUCIE remains stable and physically consistent for 100 years of autoregressive simulation with 100 ensemble members. Long‐term mean climatology from LUCIE's simulation of temperature, wind, precipitation, and humidity matches that of ERA5 data, along with the variability. We further demonstrate how well extreme weather events and their return periods can be estimated from a large ensemble of long‐term simulations. We further discuss an improved training strategy with a hard‐constrained first‐order integrator to suppress autoregressive error growth, a novel spectral regularization strategy to better capture fine‐scale dynamics, and finally an optimization algorithm that enables data‐limited (as low as 2 years of 6‐hourly data) training of the emulator without losing stability and physical consistency. Finally, we provide a scaling experiment to compare the long‐term bias of LUCIE with respect to the number of training samples. Importantly, LUCIE is an easy to use model that can be trained in just 2.4 hr on a single A‐100 GPU, allowing for multiple experiments that can explore important scientific questions that could be answered with large ensembles of long‐term simulations, for example, the impact of different variables on the simulation, dynamic response to external forcing, and estimation of extreme weather events, amongst others. 

Abstract Building upon recent advancements in AI‐driven atmospheric emulation, we present a novel framework for AI‐based ocean emulation, downscaling, and bias correction, with a specific focus on high‐resolution modeling of the regional ocean in the Gulf of Mexico. Emulating regional ocean dynamics poses distinct challenges due to intricate bathymetry, complex lateral boundary conditions, and inherent limitations of deep learning models, including instability and the potential for hallucinations. In this study, we introduce a deep learning framework that autoregressively integrates ocean surface variables at 8 km spatial resolution over the Gulf of Mexico, maintaining physical consistency over decadal time scales. Simultaneously, the framework downscales and bias‐corrects the outputs to 4 km resolution using a physics‐informed generative model. Our approach demonstrates short‐term predictive skill comparable to high‐resolution physics‐based simulations, while also accurately capturing long‐term statistical properties, including temporal mean and variability.